1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China; 2. University of Chinese Academy Sciences, Beijing 100049, China
Abstract：In order to solve tracking failure problem caused by target occlusion, appearance variation and long time tracking in practical monitoring, an object tracking algorithm based on RANdom SAmpling Consensus (RANSAC) estimation was proposed. Firstly, the local invariant feature set in the searching area was extracted. Then the object features were separated from the feature set by using the transfer property of feature matching and non-parametric learning algorithm. At last, the RANSAC estimation of object features was used to track the object location. The algorithm was tested on video data sets with different scenarios and analyzed by using three analysis indicators including accuracy, recall and comprehensive evaluation (F1-Measure). The experimental results show that the proposed method improves target tracking accuracy and overcomes track-drift caused by long time tracking.
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2016, Vol. 36 
